tooluniverse-functional-genomics-screens

Prioritize CRISPR and shRNA screen hits using DepMap, gnomAD, pathway, and druggability evidence.

1.7k|254|Updated Mar 3, 2025

Search Tags:#pathway enrichment #druggability #crispr #target prioritization #DepMap #functional genomics #genetic screens

Ready to run

npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-functional-genomics-screens

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: tooluniverse-functional-genomics-screens
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-functional-genomics-screens
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-functional-genomics-screens

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Genetic screens (CRISPR-KO, CRISPRi, shRNA) produce long hit lists full of false positives, and researchers need a systematic way to validate, rank, and turn those hits into a shortlist of druggable targets.

Core Features & Use Cases

Hit Validation: Classify hits as pan-essential, selectively essential, or context-specific using DepMap dependency scores, gnomAD constraint metrics (pLI, LOEUF), and UniProt function.
Pathway & Network Analysis: Run Reactome pathway enrichment and STRING network analysis to find convergent biology across hits.
Druggability & Clinical Evidence: Query DGIdb for drug-gene interactions, CIViC for clinical evidence, and COSMIC for mutation frequencies.
Prioritized Scoring: Produce a tiered target report (T1-T4) using a 0-18 multi-dimensional scoring rubric.
Use Case: Given 200 hits from a CRISPR screen in a lung cancer cell line, validate each against DepMap, enrich pathways, check druggability, and output a ranked shortlist of high-confidence targets.

Quick Start

Analyze my CRISPR screen hit list of genes TP53, KRAS, and EGFR from a lung cancer cell line and produce a prioritized target report.

Frequently Asked Questions about tooluniverse-functional-genomics-screens

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I prioritize hits from a CRISPR screen?▼

Validate each hit against DepMap dependency scores and gnomAD constraint metrics, then run Reactome pathway enrichment and STRING network analysis. Score hits on essentiality selectivity, pathway convergence, druggability, clinical evidence, and literature support to produce tiered rankings.

How do I get DepMap Chronos dependency scores for a gene?▼

Use the bundled depmap_gene_dependency.py script, which downloads the DepMap Public CRISPRGeneEffect.csv once and queries by gene or cell line. Chronos scores below -0.5 indicate dependency and below -1.0 indicate strong essentiality.

What databases are used to assess gene druggability?▼

DGIdb provides drug-gene interactions and druggability categories such as kinases and GPCRs. For high-priority hits, clinical trial searches and PubMed queries find novel inhibitors not yet cataloged in DGIdb.

Does this workflow work for non-cancer genetic screens?▼

It works but with caveats, since DepMap covers roughly 1000 cancer cell lines and is less informative for non-cancer contexts. For non-cancer screens, weight gnomAD constraint and pathway evidence more heavily than DepMap essentiality.

What are the limitations of CRISPR screen hit interpretation?▼

The workflow does not perform raw screen analysis, which requires upstream tools like MAGeCK or BAGEL. STRING interactions represent associations rather than causal relationships, and shRNA screens need a higher validation bar due to off-target effects.